TH‐EF‐BRB‐05: Dynamic Blade Collimation for Image Quality Improvement and Dose Reduction in CBCT Guidance
Bibliographic record
Abstract
Purpose: The focus of this work is the development of a novel blade collimation system enabling volume‐of‐interest (VOI) CBCT using the kV source on a TrueBeam linear accelerator (linac). Advantages of the system are assessed, particularly with regard to reduction and localization of dose, as well as improvement of image quality. Methods: A four blade dynamic kV collimator was developed to track a VOI during a CBCT acquisition. The system is controlled using a Raspberry Pi computer placed within the linac gantry. The current prototype is capable of tracking an arbitrary volume defined by the treatment planner for subsequent CBCT guidance. During gantry rotation, the collimator tracks the VOI with adjustment of position and dimension. CBCT image quality was investigated as a function of collimator dimension, while maintaining the same dose to the VOI, for a 20 cm diameter cylindrical water phantom with a 9 mm diameter bone insert centered on isocenter. Dose distributions for various anatomical sites were modeled using a dynamic BEAMnrc library and DOSXYZnrc. The resulting VOI dose distributions were compared to full‐field distributions to quantify dose reduction and localization to the target volume. Results: Preliminary measurements show contrast increase by a factor of 1.3 and noise reduction by a factor of 1.7, for CBCT. This results in a increase in contrast‐to‐noise ratio by a factor of approximately 2.2. Depending upon the anatomical site, dose was reduced to 15%–80% of the full field value along the central axis plane and down to less than 1% along the axial planes. Conclusion: The present system allows CBCT imaging of a planner‐defined volume, offering both image quality improvement and reduction of imaging dose for the patient. Current work involves the development of fluence modulation for VOI CBCT. This work was supported through a research partnership with Varian Medical, Incorporated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".